David Curry

MLOps covers some of the same areas as DataOps, but is more focused on the continuous training of machine learning models through automation.

A new EDM Council report discusses best practices and standards for cloud data management and looks at how organizations around the world are following them.

Smart cities face the same data access and sharing problems businesses deal with everyday. In both environments, automated data pipelines can help.

Automation enabled by data engineers can help overcome common data pipeline challenges, which delivers benefits to all involved.

Automated data pipelines are essential for machine learning operations (MLOps), as the amount of data collected and analyzed exceeds most other IT operations.

Enterprises are taking different approaches to addressing their cloud spend. Some are moving off of cloud, but more are looking for ways to optimize their cloud costs.

Automated data pipeline tools can help businesses increase their speed to market, by reducing the amount of manual ETL required.

Steps include selecting a storage strategy, migrating data to cloud, and optimizing performance.

The Rapid Migration Program (RaMP) combines Google cloud products, engagement models, and best practices into a single program.

One of the main upgrades with the migration is an “industrial metaverse” that uses real-time data from simulated digital twin factories to inform physical factory improvements.

FinOps is becoming one of the main ways leading-edge companies are getting a handle on cloud spend and maximum value out of their cloud services.

Cloud service providers and others are attempting to address cloud security issues that arise due to the traditional patchwork-style of cyber security. They intend to do this by building cloud-native application protection platforms (CNAPP), a single platform to secure cloud-native workloads.